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• W3165707751 abstract "Cluster discrimination is an effective pretext task for unsupervised representation learning, which often consists of two phases: clustering and discrimination. Clustering is to assign each instance a pseudo label that will be used to learn representations in discrimination. The main challenge resides in clustering since prevalent clustering methods (e.g., k-means) have to run in a batch mode. Besides, there can be a trivial solution consisting of a dominating cluster. To address these challenges, we first investigate the objective of clustering-based representation learning. Based on this, we propose a novel clustering-based pretext task with online textbf{Co}nstrained textbf{K}-mtextbf{e}ans (textbf{CoKe}). Compared with the balanced clustering that each cluster has exactly the same size, we only constrain the minimal size of each cluster to flexibly capture the inherent data structure. More importantly, our online assignment method has a theoretical guarantee to approach the global optimum. By decoupling clustering and discrimination, CoKe can achieve competitive performance when optimizing with only a single view from each instance. Extensive experiments on ImageNet and other benchmark data sets verify both the efficacy and efficiency of our proposal. Code is available at url{https://github.com/idstcv/CoKe}." @default.
• W3165707751 created "2021-06-07" @default.
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• W3165707751 date "2021-05-24" @default.
• W3165707751 modified "2023-09-26" @default.
• W3165707751 title "Unsupervised Visual Representation Learning by Online Constrained K-Means" @default.
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• W3165707751 doi "https://doi.org/10.48550/arxiv.2105.11527" @default.
• W3165707751 hasPublicationYear "2021" @default.
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